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Novel Texture Descriptor Family for Face Recognition

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Artificial Intelligence and Soft Computing (ICAISC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11509))

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Abstract

This paper presents a novel image descriptor family. The shaped local binary patterns are an extension of the popular local binary patterns (LBPs). It takes into consideration larger neighbourhoods of the central pixel. The main novelty is that this descriptor allows using varying shapes of the neighbourhood instead of just one point. This property ensures better robustness and gives the opportunity to fine-tune the descriptor for a given task. We evaluate the descriptor on the face recognition task in the frame of an application for recognition of real-world face images. The results on two standard face corpora show improved performance over the basic LBP method.

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Notes

  1. 1.

    We used \(S-LBP_{8,5,6}\) configuration.

References

  1. Ahonen, T., Hadid, A., Pietikäinen, M.: Face recognition with local binary patterns. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3021, pp. 469–481. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24670-1_36

    Chapter  Google Scholar 

  2. Ahonen, T., Hadid, A., Pietikainen, M.: Face description with local binary patterns: application to face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 28(12), 2037–2041 (2006)

    Article  Google Scholar 

  3. Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)

    Article  MathSciNet  Google Scholar 

  4. Lenc, L., Král, P.: Unconstrained facial images: database for face recognition under real-world conditions. In: Lagunas, O.P., Alcántara, O.H., Figueroa, G.A. (eds.) MICAI 2015. LNCS (LNAI), vol. 9414, pp. 349–361. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27101-9_26

    Chapter  Google Scholar 

  5. Lenc, L., Král, P.: Local binary pattern based face recognition with automatically detected fiducial points. Integr. Comput.-Aided Eng. 23(2), 129–139 (2016). https://doi.org/10.3233/ICA-150506

    Article  Google Scholar 

  6. Li, W., Fu, P., Zhou, L.: Face recognition method based on dynamic threshold local binary pattern. In: Proceedings of the 4th International Conference on Internet Multimedia Computing and Service, pp. 20–24. ACM (2012)

    Google Scholar 

  7. Nanni, L., Lumini, A., Brahnam, S.: Survey on LBP based texture descriptors for image classification. Expert Syst. Appl. 39(3), 3634–3641 (2012)

    Article  Google Scholar 

  8. Ojala, T., Pietikainen, M., Harwood, D.: Performance evaluation of texture measures with classification based on kullback discrimination of distributions. In: 1994 Proceedings of the 12th IAPR International Conference on Pattern Recognition. Vol. 1-Conference A: Computer Vision & Image Processing, vol. 1, pp. 582–585. IEEE (1994)

    Google Scholar 

  9. Phillips, P.J., Wechsler, H., Huang, J., Rauss, P.: The FERET database and evaluation procedure for face recognition algorithms. Image Vis. Comput. 16(5), 295–306 (1998)

    Article  Google Scholar 

  10. Smith, J.R.: Integrated spatial and feature image systems: retrieval, analysis and compression. Ph.D. thesis, Columbia University (1997)

    Google Scholar 

  11. Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)

    Article  MathSciNet  Google Scholar 

  12. Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012)

    Article  Google Scholar 

  13. Wang, L., He, D.C.: Texture classification using texture spectrum. Pattern Recogn. 23(8), 905–910 (1990)

    Article  Google Scholar 

  14. Wolf, L., Hassner, T., Taigman, Y., et al.: Descriptor based methods in the wild. In: Workshop on Faces in ‘Real-Life’ Images: Detection, Alignment, and Recognition (2008)

    Google Scholar 

  15. Yao, B., Ai, H., Ijiri, Y., Lao, S.: Domain-partitioning rankboost for face recognition. In: 2007 IEEE International Conference on Image Processing, ICIP 2007, vol. 1, p. I-129. IEEE (2007)

    Google Scholar 

  16. Zhang, B., Gao, Y., Zhao, S., Liu, J.: Local derivative pattern versus local binary pattern: face recognition with high-order local pattern descriptor. IEEE Trans. Image Process. 19(2), 533–544 (2010)

    Article  MathSciNet  Google Scholar 

  17. Zhang, W., Shan, S., Gao, W., Chen, X., Zhang, H.: Local Gabor binary pattern histogram sequence (LGBPHS): a novel non-statistical model for face representation and recognition. In: 2005 Tenth IEEE International Conference on Computer Vision, ICCV 2005, vol. 1, pp. 786–791. IEEE (2005)

    Google Scholar 

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Acknowledgment

This work has been partly supported by the project LO1506 of the Czech Ministry of Education, Youth and Sports.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic

    Pavel Král & Ladislav Lenc

  2. NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia, Pilsen, Czech Republic

    Pavel Král & Ladislav Lenc

Authors
  1. Pavel Král
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  2. Ladislav Lenc
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Corresponding author

Correspondence to Ladislav Lenc .

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Král, P., Lenc, L. (2019). Novel Texture Descriptor Family for Face Recognition. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2019. Lecture Notes in Computer Science(), vol 11509. Springer, Cham. https://doi.org/10.1007/978-3-030-20915-5_4

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  • DOI: https://doi.org/10.1007/978-3-030-20915-5_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-20914-8

  • Online ISBN: 978-3-030-20915-5

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